After considering the methods described above in each scenario, we classify any point as a change if its posterior probability of being a change is greater than 0.5. Then, we compute the overall misclassification rate, sensitivity (true positive rate), and specificity (true negative rate) of classifying points as change points. These metrics give us information on the accuracy of identifying points as change points. In addition, we calculate the adjusted Rand index [1]} (ARI) between the estimated partition based on change points and the true partition. This metric illustrates how well each method does at recovering the true partition of contiguous clusters.
